OpenShift AI Fundamentals for Developers

As organizations rush to integrate artificial intelligence into their products, developers need a reliable, enterprise-grade environment to manage the entire machine learning lifecycle. This text-based course guides you through OpenShift AI, explaining how to transition from raw data to production-ready AI applications. You will learn how to navigate the platform, manage resources, and deploy models efficiently. By completing this course, you will understand how to configure workspaces, run data science pipelines, and serve models as scalable APIs. You will gain the practical knowledge required to collaborate with data scientists and seamlessly integrate predictive models into your software applications. What you'll learn: - Understand the core architecture of OpenShift AI and its primary components - Configure secure workbench environments and manage persistent storage for your datasets - Build and run reproducible data science pipelines to automate model training - Serve machine learning models as scalable APIs for real-time inference - Monitor model performance and manage resource allocation within your cluster - Apply modern GitOps principles to track and version your AI assets We start with the absolute basics, clarifying key machine learning operations (MLOps) terminology and platform concepts. From there, you will read through step-by-step configuration guides, explore code examples, and study deployment patterns that mirror real-world production environments. This course is designed for software developers, system administrators, and aspiring DevOps engineers who are new to OpenShift AI. No prior machine learning experience is required, though a basic familiarity with container concepts and Kubernetes is helpful. Start reading today to master the essentials of enterprise AI deployment.